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The Machine Learning for Integrative Genomics team at Institut Pasteur, headed by Laura Cantini, works at the interface of machine learning and biology, developing innovative machine learning
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-off companies. CONTEXT AND MISSION We are seeking a postdoc to join the Quantum Machine Learning team (QML-CVC) in beautiful Barcelona. The QML-CVC team (https://qml.cvc.uab.es /) is part of
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, United States of America [map ] Subject Area: Computational Science / Artificial Intelligence/Machine Learning Appl Deadline: (posted 2025/11/19, listed until 2026/01/26) Position Description: Apply Position Description
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materials and technologies. Using advanced computational modeling and machine learning, we seek to elucidate the mechanisms governing the self-assembly of lignin in different solvents and the formation
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 1 hour ago
Organization National Aeronautics and Space Administration (NASA) Reference Code 0202-NPP-MAR26-JPL-HelioSci How to Apply All applications must be submitted in Zintellect Please visit the NASA Postdoctoral Program website for application instructions and requirements: How to Apply | NASA...
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growing and supportive team with internationally recognized expertise in data management and machine learning. The group has a strong network of national and international collaborators in both academia and
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approaches for using machine learning to analyze X-ray data, particularly Resonant Inelastic X-ray Scattering (RIXS). The position will collaborate with experts in RIXS experiments (Mark Dean), computational
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technologies generate unprecedented volumes of molecular data at cellular resolution, opening new avenues for the application of machine learning to fundamental biological problems. The postdoctoral researchers
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, France [map ] Subject Area: Machine Learning / Machine Learning Appl Deadline: 2025/12/12 11:59PM ** (posted 2025/10/21, listed until 2026/04/21) Position Description: Apply *** the listing date
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(iii) complex architectures with tightly coupled components hinder modular adaptation. To address these limitations, we research a physics-guided machine learning framework that integrates physical